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Found 18 papers, 9 papers with code
基于Self-Attention的句法感知汉语框架语义角色标注(Syntax-Aware Chinese Frame Semantic Role Labeling Based on Self-Attention)
框架语义角色标注(Frame Semantic Role Labeling, FSRL)是基于FrameNet标注体系的语义分析任务。语义角色标注通常对句法有很强的依赖性, 目前的语义角色标注模型大多基于双向长短时记忆网络Bi-LSTM, 虽然可以获取句子中的长距离依赖信息, 但无法很好获取句子中的句法信息。因此, 引入self-attention机制来捕获句子中每个词的句法信息。实验结果表明, 该模型在CFN(Chinese FrameNet, 汉语框架网)数据集上的F1达到83. 77%, 提升了近11%。
Large Language Models Are Zero-Shot Text Classifiers
Retrained large language models (LLMs) have become extensively used across various sub-disciplines of natural language processing (NLP).
Counting Manatee Aggregations using Deep Neural Networks and Anisotropic Gaussian Kernel
In this paper, we propose a deep learning based crowd counting approach to automatically count number of manatees within a region, by using low quality images as input.
Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems
Concretely, we employ multi-task learning to adaptively combine the optimization of relaxed and full targets, which refers to metrics Recall@m@k and OPA respectively.
Set-level Guidance Attack: Boosting Adversarial Transferability of Vision-Language Pre-training Models
Vision-language pre-training (VLP) models have shown vulnerability to adversarial examples in multimodal tasks.
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs.
KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks
Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes.
Shale oil production prediction and fracturing optimization based on machine learning
The results reveal that DNN exhibit best production prediction accuracy compared to RF and SVM.
Benchmarks for Corruption Invariant Person Re-identification
When deploying person re-identification (ReID) model in safety-critical applications, it is pivotal to understanding the robustness of the model against a diverse array of image corruptions.
Ranked #1 on
Cross-Modal Person Re-Identification
on RegDB-C
(mINP (Visible to Thermal) metric)
Cross-Modal Person Re-Identification
Generalizable Person Re-identification
ContextNet: A Click-Through Rate Prediction Framework Using Contextual information to Refine Feature Embedding
In this paper, We propose a novel CTR Framework named ContextNet that implicitly models high-order feature interactions by dynamically refining each feature's embedding according to the input context.
Ranked #14 on
Click-Through Rate Prediction
on Criteo
Leaf-FM: A Learnable Feature Generation Factorization Machine for Click-Through Rate Prediction
For example, the continuous features are usually transformed to the power forms by adding a new feature to allow it to easily form non-linear functions of the feature.
GLSD: The Global Large-Scale Ship Database and Baseline Evaluations
In this paper, we introduce a challenging global large-scale ship database (called GLSD), designed specifically for ship detection tasks.
SSCAN: A Spatial-spectral Cross Attention Network for Hyperspectral Image Denoising
In this study, we propose a novel HSI denoising network, termed SSCAN, that combines group convolutions and attention modules.
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
We also turn the feed-forward layer in DNN model into a mixture of addictive and multiplicative feature interactions by proposing MaskBlock in this paper.
Ranked #8 on
Click-Through Rate Prediction
on Criteo
GateNet: Gating-Enhanced Deep Network for Click-Through Rate Prediction
Inspired by these observations, we propose a novel model named GateNet which introduces either the feature embedding gate or the hidden gate to the embedding layer or hidden layers of DNN CTR models, respectively.
Ranked #20 on
Click-Through Rate Prediction
on Criteo
Correct Normalization Matters: Understanding the Effect of Normalization On Deep Neural Network Models For Click-Through Rate Prediction
Normalization has become one of the most fundamental components in many deep neural networks for machine learning tasks while deep neural network has also been widely used in CTR estimation field.
